Triple Homogeneously Weighted Moving Average Charts for Monitoring Process Dispersion

Abstract

Homogeneously weighted moving average ( H W M A ) charts have recently achieved popularity for monitoring small changes in process parameters (location and/or dispersion). Furthermore, the D H W M A (double H W M A ) and T H W M A (triple H W M A ) are the advanced versions of the H W M A charts. The H W M A chart for the process dispersion is designed to detect only the upward (one-sided) shift (i.e., process deterioration). Employing a two-sided chart for concurrently detecting both process improvement and process deterioration is an important aspect of statistical process monitoring. By taking this point as motivation, one and two-sided T H W M A charts (symbolized as the T H W M A V ) are proposed for monitoring the process dispersion. The Monte Carlo simulations are performed to investigate the performance behavior of the T H W M A V charts in terms of certain performance indicators, including A R L , S D R L , E Q L , R A R L , and P C I . The comparison among the T H W M A V versus existing charts ( D H W M A V , H W M A V , T E W M A V , D E W M A V , and E W M A V ) indicates that the T H W M A V charts outperform the existing charts. Finally, a dataset is also analyzed to illustrate the implementation of the T H W M A V charts.